The task of computing connected components becomes more difficult when graphs are very large, or when they are dynamic, meaning the edge set changes over time subject to a stream of edge insertions and deletions. Applications on large graphs include metagenome assembly tasks that may include hundreds of millions of genes with complex relations [1]}, and large-scale clustering, which is a common machine learning challenge [2]}. Applications using dynamic graphs include identifying objects from a video feed rather than a static image [3]}, or tracking communities in social networks that change as users add or delete friends [4]}, [5]}. And of course graphs can be both large and dynamic. Indeed, Sahu et al.'s [6]} survey reports that a majority of industry respondents work with large graphs (\(> 1\)  million nodes or \(> 1\)  billion edges) and a majority work with graphs that change over time.
